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A systematic in silico mining of the mechanistic implications and therapeutic potentials of estrogen receptor (ER)-? in breast cancer.

ABSTRACT: Estrogen receptor (ER)-? has long been a potential target in ER-?-positive breast cancer therapeutics. In this study, we integrated ER-?-related bioinformatic data at different levels to systematically explore the mechanistic and therapeutic implications of ER-?. Firstly, we identified ER-?-interacting proteins and target genes of ER-?-regulating microRNAs (miRNAs), and analyzed their functional gene ontology (GO) annotations of those ER-?-associated proteins. In addition, we predicted ten consensus miRNAs that could target ER-?, and screened candidate traditional Chinese medicine (TCM) compounds that might hit diverse conformations of ER-? ligand binding domain (LBD). These findings may help to uncover the mechanistic implications of ER-? in breast cancer at a systematic level, and provide clues of miRNAs- and small molecule modulators- based strategies for future ER-?-positive breast cancer therapeutics.

SUBMITTER: Li X 

PROVIDER: S-EPMC3948898 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A systematic in silico mining of the mechanistic implications and therapeutic potentials of estrogen receptor (ER)-α in breast cancer.

Li Xin X   Sun Rong R   Chen Wanpeng W   Lu Bangmin B   Li Xiaoyu X   Wang Zijie Z   Bao Jinku J  

PloS one 20140310 3

Estrogen receptor (ER)-α has long been a potential target in ER-α-positive breast cancer therapeutics. In this study, we integrated ER-α-related bioinformatic data at different levels to systematically explore the mechanistic and therapeutic implications of ER-α. Firstly, we identified ER-α-interacting proteins and target genes of ER-α-regulating microRNAs (miRNAs), and analyzed their functional gene ontology (GO) annotations of those ER-α-associated proteins. In addition, we predicted ten conse  ...[more]

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